Software Assistance for Needle Based Intervention

SAFIR supports the whole interventional workflow from planning to assessment utilizing segmentation, registration, simulation, optimization and visualization methodology. To facilitate fast deployment of new applications targeted at specific clinical questions or scientific projects, SAFIR is designed as a modular framework, which allows for re-using information processing methods and customization of algorithms.

Early countermeasures against ineffective therapies

Deep Learning for Image Understanding at SPIE Medical Imaging 2018

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26.2.2018

Early countermeasures against ineffective therapies

What effect does a particular cancer medicine or radiation therapy have on patients? To find out, physicians use CT images to determine whether a tumor’s size changes during the course of treatment. In the PANTHER project, a joint team of experts aims at gaining further valuable information from these images. In the future, doctors will be able to find out at an early stage whether a cancer treatment is effective or should be changed. The Fraunhofer Institute for Medical Image Computing MEVIS, with its branches in Bremen and Lübeck, is an essential project partner. This spring, the project team will present the interim results from the first project period.

Deep Learning for Image Understanding at SPIE Medical Imaging 2018

Fraunhofer MEVIS researchers Markus Wenzel and Hans Meine instructed a 1-day-course on “Deep Learning for Image Understanding” on Saturday, February 10 as part of this year‘s SPIE Medical Imaging Conference held February 10–15 in Houston, Texas.

We mourn for Conrad Naber

“The Future of Medical Ultrasound” at Charité Berlin

About 100 participants and international experts from medicine, science, and industry discussed the potential of ultrasound technologies and solutions for medical diagnosis and therapy at the Charité Berlin in a 2-day symposium and a 1-day expert meeting on January 17–19, 2018.